Prediction of Mortality and Intervention in COVID-19 Patients Using Generative Adversarial Networks

Uiwon Hwang, Euideuk Hwang, Minsoo Kang, Sungroh Yoon
Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, PMLR 184:91-99, 2022.

Abstract

The COVID-19 pandemic hits worldwide with a significant number of deaths and poses a major threat to public health. Accurate predictions of the risk of death and medical interventions are crucial for the survival of infected patients and the distribution of limited medical resources. Although machine learning classifiers can be used to predict mortality and medical interventions, it is problematic to employ the methods because training data are limited whose attributes may be missing and classes may be imbalanced. To effectively cope with these problems, we construct HexaGAN with a hint mechanism to predict the survival of the patients and medical interventions such as intubation and supplemental oxygen. In experiments, our method outperforms combinations of existing techniques for limited data problems. Notably, our method showed about twice higher performance than benchmarks in predicting deceased patients correctly. We anticipate that our approach could help provide appropriate treatments on time, allocate limited medical resources efficiently, and ultimately reduce the mortality rate of COVID-19 patients.

Cite this Paper

BibTeX
@InProceedings{pmlr-v184-hwang22a, title = {Prediction of Mortality and Intervention in COVID-19 Patients Using Generative Adversarial Networks}, author = {Hwang, Uiwon and Hwang, Euideuk and Kang, Minsoo and Yoon, Sungroh}, booktitle = {Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022}, pages = {91--99}, year = {2022}, editor = {Xu, Peng and Zhu, Tingting and Zhu, Pengkai and Clifton, David A. and Belgrave, Danielle and Zhang, Yuanting}, volume = {184}, series = {Proceedings of Machine Learning Research}, month = {22 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v184/hwang22a/hwang22a.pdf}, url = {https://proceedings.mlr.press/v184/hwang22a.html}, abstract = {The COVID-19 pandemic hits worldwide with a significant number of deaths and poses a major threat to public health. Accurate predictions of the risk of death and medical interventions are crucial for the survival of infected patients and the distribution of limited medical resources. Although machine learning classifiers can be used to predict mortality and medical interventions, it is problematic to employ the methods because training data are limited whose attributes may be missing and classes may be imbalanced. To effectively cope with these problems, we construct HexaGAN with a hint mechanism to predict the survival of the patients and medical interventions such as intubation and supplemental oxygen. In experiments, our method outperforms combinations of existing techniques for limited data problems. Notably, our method showed about twice higher performance than benchmarks in predicting deceased patients correctly. We anticipate that our approach could help provide appropriate treatments on time, allocate limited medical resources efficiently, and ultimately reduce the mortality rate of COVID-19 patients.} }
Endnote
%0 Conference Paper %T Prediction of Mortality and Intervention in COVID-19 Patients Using Generative Adversarial Networks %A Uiwon Hwang %A Euideuk Hwang %A Minsoo Kang %A Sungroh Yoon %B Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022 %C Proceedings of Machine Learning Research %D 2022 %E Peng Xu %E Tingting Zhu %E Pengkai Zhu %E David A. Clifton %E Danielle Belgrave %E Yuanting Zhang %F pmlr-v184-hwang22a %I PMLR %P 91--99 %U https://proceedings.mlr.press/v184/hwang22a.html %V 184 %X The COVID-19 pandemic hits worldwide with a significant number of deaths and poses a major threat to public health. Accurate predictions of the risk of death and medical interventions are crucial for the survival of infected patients and the distribution of limited medical resources. Although machine learning classifiers can be used to predict mortality and medical interventions, it is problematic to employ the methods because training data are limited whose attributes may be missing and classes may be imbalanced. To effectively cope with these problems, we construct HexaGAN with a hint mechanism to predict the survival of the patients and medical interventions such as intubation and supplemental oxygen. In experiments, our method outperforms combinations of existing techniques for limited data problems. Notably, our method showed about twice higher performance than benchmarks in predicting deceased patients correctly. We anticipate that our approach could help provide appropriate treatments on time, allocate limited medical resources efficiently, and ultimately reduce the mortality rate of COVID-19 patients.
APA
Hwang, U., Hwang, E., Kang, M. & Yoon, S.. (2022). Prediction of Mortality and Intervention in COVID-19 Patients Using Generative Adversarial Networks. Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, in Proceedings of Machine Learning Research 184:91-99 Available from https://proceedings.mlr.press/v184/hwang22a.html.

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